Information processing apparatus, information processing method, non-transitory computer-readable storage medium

ABSTRACT

An information processing apparatus comprises a conversion unit configured to convert an element array, in each region set so as to partially overlap at least one set of adjacent regions in input data, into a lower dimension element array of which dimension is lower than that of the element array; a generation unit configured to generate a connected element by connecting some or all of the lower dimension element arrays converted by the conversion unit so that an overlapping portion in each of the lower dimension element arrays will be shared; and a calculation unit configured to obtain a feature amount of the input data based on convolution of the connected elements and a weight coefficient.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to an information processing techniqueusing convolution.

Description of the Related Art

In recent years, an image recognition technique using a convolutionalneural network (CNN) has gained attention. It is known that training aCNN by using a large amount of images will allow the CNN to achieve highrecognition accuracy, and this technique has been used and applied tovarious kinds of fields.

When a trained CNN is to actually operate and is to be used as some kindof a processing system, its execution speed is important. For example,real-time processing would be required if authentication of a person whohas been captured by a monitoring camera is to be performed, and aresult would need to be returned within a length of time that would notdegrade user convenience if a specific image is to be searched frompreviously captured and stored images.

In general, image convolution takes up a large part of a CNN operation,and the speedup of convolution is necessary to increase the speed of theCNN operation. Although a method of increasing the operation speed byapproximation calculation can be employed, since this method caninfluence the recognition accuracy, it is more desirable to increase theoperation speed by a method that will not change the arithmeticoperation result.

The specification of U.S. Pat. No. 8,160,388 discloses, in a case inwhich a specific filter is to be used in image convolution, a method ofreducing the operation count when the filter is to be applied byseparating the filter in a vertical direction and a horizontal directionand executing the operation separately. According to this method,although applicable filters are limited, the speedup of the operationcan be expected since the operation count itself will be reduced.

The specification of U.S. Pat. No. 7,634,137 discloses a method ofgenerating a transformation matrix in which a partial region of a filterapplication target image is rearranged to have the same arrangement asthe filter so as to result in a matrix product with the filter matrix.The arithmetic operation tends to be hindered by discontinuous access toa partial region of the image. This method can perform a high-speedarithmetic operation since a continuous memory access to an elementbecomes possible at the time of a sum product operation by resulting ina matrix product.

However, these methods are insufficient in the point of view of speedup.In the method disclosed in the specification of U.S. Pat. No. 8,160,388,since two convolutions need to be executed sequentially in separatevertical and horizontal directions, the access to the image willincrease compared to that in a normal two-dimensional convolution, andthe processing speed may degrade as a result. In the method disclosed inthe specification of U.S. Pat. No. 7,634,137, the total memory accessamount increases since rearrangement is performed redundantly byallowing the pixel values of the image to overlap, and the processingspeed may degrade as a result.

In particular, the arithmetic operational performance of a GPU (GraphicProcessing Unit) is higher than that of a CPU (Central Processing Unit).Hence, memory access can hinder the processing speed when an arithmeticoperation is to be performed by using a GPU, and pose problem forspeedup.

SUMMARY OF THE INVENTION

The present invention provides a technique for executing convolutionprocessing on input data at a speed higher than that of a related art.

According to the first aspect of the present invention, there isprovided an information processing apparatus comprising: a conversionunit configured to convert an element array, in each region set so as topartially overlap at least one set of adjacent regions in input data,into a lower dimension element array of which dimension is lower thanthat of the element array; a generation unit configured to generate aconnected element by connecting some or all of the lower dimensionelement arrays converted by the conversion unit so that an overlappingportion in each of the lower dimension element arrays will be shared;and a calculation unit configured to obtain a feature amount of theinput data based on convolution of the connected elements and a weightcoefficient.

According to the second aspect of the present invention, there isprovided an information processing method performed by an informationprocessing apparatus, the method comprising: converting an elementarray, in each region set so as to partially overlap at least one set ofadjacent regions in input data, into a lower dimension element array ofwhich dimension is lower than that of the element array; generating aconnected element by connecting some or all of the lower dimensionelement arrays converted in the converting so that an overlappingportion in each of the lower dimension element arrays will be shared;and obtaining a feature amount of the input data based on convolution ofthe connected elements and a weight coefficient.

According to the third aspect of the present invention, there isprovided a non-transitory computer-readable storage medium storing acomputer program for causing a computer to function as a conversion unitconfigured to convert an element array, in each region set so as topartially overlap at least one set of adjacent regions in input data,into a lower dimension element array of which dimension is lower thanthat of the element array of a lower dimension; a generation unitconfigured to generate a connected element by connecting some or all ofthe lower dimension element arrays converted by the conversion unit sothat an overlapping portion in each of the lower dimension elementarrays will be shared; and a calculation unit configured to obtain afeature amount of the input data based on convolution of the connectedelements and a weight coefficient.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments (with reference to theattached drawings).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example of the arrangement of asystem;

FIG. 2 is a block diagram showing an example of the functionalarrangement of an information processing apparatus 1;

FIG. 3 is a flowchart corresponding to an authentication function;

FIG. 4 is a block diagram showing an example of the functionalarrangement of a feature extraction module 103;

FIG. 5 is a block diagram showing an example of the functionalarrangement of a convolution processing module 111;

FIG. 6 is a flowchart of convolution processing;

FIGS. 7A to 7C are schematic views showing a connected one-dimensionalpixel array generation processing performed by a conversion module 123;

FIG. 8 is a view schematically showing convolution processing performedby an arithmetic operation module 125;

FIG. 9 is a flowchart corresponding to a registration function;

FIGS. 10A and 10B are views for explaining the operation of a conversionmodule 123;

FIGS. 11A to 11D are views for explaining the operation of a conversionmodule 123; and

FIG. 12 is a view schematically showing convolution processing performedby an arithmetic operation module 125.

DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention will now be described withreference to the accompanying drawings. Note that the embodiments to bedescribed below are examples of detailed implementation of the presentinvention or detailed examples of the arrangement described in theappended claims.

First Embodiment

An example of the arrangement of a system according to this embodimentwill be described first with reference to the block diagram of FIG. 1.As shown in FIG. 1, the system according to this embodiment includes aninformation processing apparatus 1 and a camera 2, and the informationprocessing apparatus 1 and the camera 2 are arranged so as to be able toexecute data communication with each other via a network. The networkmay be a wireless network, a wired network, or a network combining thewireless network and the wired network.

The camera 2 will be described first. The camera 2 is a network camera(image capturing device) capable of capturing moving images and stillimages, and includes a camera unit which includes a lens and an imagesensor such as a CCD, a CMOS sensor or the like and a communicationdevice for executing data communication with the information processingapparatus 1 by connecting to the above-described network. Note thatanother kind of camera that has a communication function may be used asthe camera 2. When a moving image is captured by the camera 2, eachframe image of the moving image is output (transmitted) as a capturedimage to the information processing apparatus 1. On the other hand, whena still image is captured by the camera 2, the still image is output(transmitted) as a captured image to the information processingapparatus 1. Note that the camera 2 may be an image capturing deviceconfigured to capture visible light or an infrared light cameraconfigured to capture infrared light.

The information processing apparatus 1 will be described next. Theinformation processing apparatus 1 is a computer device such as a PC(personal computer), a tablet terminal device, a smartphone, or thelike. A CPU 11 executes processing by using computer programs and datastored in a ROM 12 and a RAM 13. This allows the CPU 11 to control theoverall operation of the information processing apparatus 1 and controlor execute each processing operation to be described later as that to beperformed by the information processing apparatus 1.

The ROM 12 is a nonvolatile memory and holds an activation program andvarious kinds of setting data of the information processing apparatus 1.The RAM 13 is a volatile memory, and includes an area for storingcomputer programs and data loaded from a secondary storage device 14 andthe ROM 12 and data (for example, an image captured by the camera 2)received from the outside (for example, the camera 2) via acommunication device 15. The RAM 13 also includes a work area which isused by the CPU 11 to execute various kinds of processing. In thismanner, the RAM 13 suitably provides various kinds of areas.

The secondary storage device 14 is a large-capacity information storagedevice represented by a hard disk drive device. The secondary storagedevice 14 stores an OS (operating system) and the computer programs anddata for allowing the CPU 11 to execute or control each of theprocessing operations to be described later as those to be performed bythe information processing apparatus 1. The data stored in the secondarystorage device 14 includes data to be described later as knowninformation. The computer programs and data stored in the secondarystorage device 14 are appropriately loaded to the RAM 13 in accordancewith the control of the CPU 11 and become targets of processing by theCPU 11.

The communication device 15 is a device for the information processingapparatus 1 to execute data communication with an external apparatus,and for example, the communication device 15 can receive a capturedimage from the camera 2 by executing data communication with the camera2.

An external output device 16 is a display device such as a liquidcrystal screen, and can display the processing results of the CPU 11 byusing images and characters. In this embodiment, the external outputdevice 16 will use images, characters, and the like to display anauthentication result of a captured image obtained by the camera 2. Notethat the external output device 16 may be a loudspeaker configured tooutput audio based on an audio signal, and in such a case, the externaloutput device 16 can output the above-described authentication result asaudio. In addition, the external output device 16 may also be an LEDlamp, and in such a case, the external output device 16 will be able tonotify a user of the above-described authentication result by lightingan LED lamp or a lighting pattern by an LED lamp. In this manner, in thecase of this embodiment, the external output device 16 may be any deviceas long as it is a device capable of notifying the user of theabove-described authentication result.

An input device 17 is formed by user interfaces such as a keyboard and amouse, and the user can operate the input device to input various kindsof instructions in the CPU 11. Note that a touch panel screen may bearranged by integrating the input device 17 and the display device.

The CPU 11, the ROM 12, the RAM 13, the secondary storage device 14, thecommunication device 15, the external output device 16, and the inputdevice 17 are all connected to a bus 18. Note that the arrangement ofthe information processing apparatus 1 shown in FIG. 1 is merely anexample, and for example, the information processing apparatus 1 mayinclude an interface for attaching/detaching a rewritable memory devicesuch as a flash memory or the like.

The information processing apparatus 1 has an authentication function toauthenticate who the object in the image captured by the camera 2 is byusing a registration dictionary that has been registered in advance anda registration function to create and register a registration dictionaryfrom the image captured by the camera 2. In order to determine whetherthe authentication function is to be operated or the registrationfunction is to be operated, for example, it may be arranged so that theuser can operate the input device 17 to instruct which of theauthentication function and the registration function is to be executed,and the CPU 11 can execute one of the authentication function and theregistration function in accordance with the instruction.

An example of the functional arrangement of the information processingapparatus 1 will be shown in the block diagram of FIG. 2. Although eachfunctional module shown in FIG. 2 may be described as the main body ofprocessing hereinafter, a function corresponding to each functionalmodule is executed by causing the CPU 11 to execute a computer programto cause the CPU 11 to implement the function corresponding to thefunctional module in practice. FIG. 3 shows a flowchart corresponding tothe authentication function. The operation of each functional moduleshown in FIG. 2 will be described hereinafter in accordance with theflowchart of FIG. 3. Note that the information processing apparatus 1has been activated before the start of the processing according to theflowchart of FIG. 3 and is in a state capable of starting the processingto be described. In addition, the camera 2 also has been activated, andis in a moving image capturing state as a monitoring camera.

In step S1011, an image acquisition module 101 acquires a captured imagereceived by the communication device 15 from the camera 2 and stored inthe RAM 13, and converts the captured image into a single-channelgrayscale image. Note that it may be arranged so that the captured imagewill be converted into a single-channel grayscale image when thecaptured image is to be stored in the RAM 13 or may be arranged so thatthe camera 2 will capture a single-channel grayscale image.

In step S1012, a face detection module 102 detects a region of an object(the face of a person in this embodiment) from the captured imageacquired by the image acquisition module 101 and extracts an image, as aface image, from the detected region. A known technique can be used asthe method for detecting a person's face from an image. For example, atechnique described in the following literature can be used.

VIOLA et al. “Robust Real-Time Face Detection.” International Journal ofComputer Vision. Vol. 57, Issue 2. 2004: 137-154.

The face image is cut out from the captured image based on thecoordinates of the face image detected from the captured image. In thiscase, image normalization is performed on the face image so that theposition of the face with respect to the face image that has been cutout will be constant. For example, scaling is performed so that thelength of a line connecting the eyes of the face will be constant withrespect to the face image to be cut out. Processing such as rotating theline so it will be horizontal with respect to the face image to be cutout will be performed. Subsequently, the following processes of stepsS1014 to step S1018 will be performed for each face image detected fromthe captured image.

In step S1014, a feature extraction module 103 uses a pre-generated CNNto extract a feature amount from each face image. An example of thefunctional arrangement of the feature extraction module 103 will bedescribed by using the block diagram of FIG. 4.

A convolution processing module 111 performs convolution processing on aface image. An example of the functional arrangement of the convolutionprocessing module 111 is shown in the block diagram of FIG. 5. Theconvolution processing performed by the convolution processing module111 on a face image will be described in accordance with the flowchartof FIG. 6.

In step S1021, an acquisition module 121 acquires a face image as inputdata. In this embodiment, a face image is a single-channel grayscaleimage. In step S1022, a setting module 122 sets a two-dimensionalsetting region on (on a two-dimensional image) a face image (face imageacquired by the acquisition module 121) so that adjacent setting regions(partial regions) will partially overlap each other. Although the sizeof the setting region will be 3 (pixels)×3 (pixels) in this embodiment,the size of the setting region is not limited to this size. The settingmodule 122 sets, for example, a setting region on each pixel position inthe face image so that the top left corner of the setting region will bepositioned at the pixel position. As a result, a plurality of settingregions can be set so that adjacent setting regions will partiallyoverlap each other. In this embodiment, since the size of each settingregion is 3 (pixels)×3 (pixels) and the setting regions are set at therespective pixel positions on the face image, adjacent setting regionswill include overlapping portions.

In step S1023, a conversion module 123 converts the two-dimensionalpixel array of each setting region set by the setting module 122 into aone-dimensional pixel array (conversion vector), and generates oneconnected one-dimensional pixel array based on the one-dimensional pixelarray of each setting region. The generation processing of a connectedone-dimensional pixel array performed by the conversion module 123 willbe described with reference to the schematic views of FIGS. 7A to 7C.Note that FIGS. 7A to 7C are views for explaining the one-dimensionalpixel array and the connected one-dimensional pixel array, and do notlimit the processing order for obtaining these arrays. That is, if thesame conversion result can be obtained ultimately, a processing orderdifferent from that shown in FIGS. 7A to 7C may be employed or the sameconversion may be implemented by combining different processes insteadof employing the processing order shown in FIGS. 7A to 7C.

As shown in FIG. 7A, the conversion module 123 converts thetwo-dimensional pixel array in a setting region 202, positioned at thetop left corner of a face image 201, into a one-dimensional pixel array203. For example, assume that elements of the leftmost column in thetwo-dimensional pixel array in the setting region 202 are denotedsequentially from above as a1, a2, and a3, elements of the second columnfrom the leftmost column are denoted sequentially from above as a4, a5,and a6, and elements of the rightmost column are denoted sequentiallyfrom above as a7, a8, and a9. In this case, the one-dimensional pixelarray 203 to be generated from such a two-dimensional pixel array willbe [a1, a2, a3, a4, a5, a6, a7, a8, and a9].

Next, as shown in FIG. 7B, the two-dimensional pixel array in a settingregion 204, obtained by shifting the setting region 202 to the right byone pixel, is converted into a one-dimensional pixel 205 array by theconversion module 123. As described above, since an overlapping regionis present between the adjacent setting regions 202 and 204, a portionoverlapping the one-dimensional pixel array 203 corresponding to thesetting region 204 will be generated as a result in the one-dimensionalpixel array 205 corresponding to the setting region 204. Therefore, theconversion module 123 will acquire, from the one-dimensional pixel array205, a portion 290 (a region indicated by slanted lines in FIG. 7B)which does not overlap the one-dimensional pixel array 203 in theone-dimensional pixel array 205. Subsequently, the conversion module 123will generate a connected one-dimensional pixel array 299 obtained byconnecting the acquired portion 290 to the right of the one-dimensionalpixel array 203. That is, this connected one-dimensional pixel arrayshares the elements belonging to a region where the setting regionsoverlap.

In this manner, in the one-dimensional pixel array corresponding to asetting region A obtained by shifting the setting region 202 to theright by N (N is an integer equal to or more than 2), a portion whichdoes not overlap the one-dimensional pixel array corresponding to asetting region B (a setting region obtained by shifting the settingregion 202 to the right by (N−1) pixels) adjacent to the setting regionA on the left is set as the connection target, and the connection targetis connected to the right of the connected one-dimensional pixel array.In this case, if the setting region A is the rightmost setting region ofthe face image and the connection target of the setting region A isconnected to the connected one-dimensional pixel array, thetwo-dimensional pixel array in a setting region 207 obtained by shiftingthe setting region 202 below by one pixel is converted into aone-dimensional pixel array 208 as shown in FIG. 7C. Although thesetting region 202 and the setting region 207 have a region thatoverlaps each other here, the overlapping portion will be heldredundantly so that the elements of the connected one-dimensional pixelarray can be continuously accessed in the subsequent processing. Thatis, as shown in FIG. 7C, the conversion module 123 connects theone-dimensional pixel array 208 to the right of the connectedone-dimensional pixel array 299 at this point. Subsequently, in the samemanner, in a one-dimensional pixel array corresponding to the settingregion A obtained by shifting the setting region 207 to the right by Npixels, a portion which does not overlap the one-dimensional pixel arraycorresponding to the setting region B adjacent to the setting region Aon the left is set as the connection target, and the connection targetis connected to the right of the connected one-dimensional pixel array.Subsequently, in this manner, a connected one-dimensional pixel array isgenerated by connecting all or some of the one-dimensional pixel arrayscorresponding to the respective setting regions. In this manner, in thisembodiment, from the one-dimensional pixel array of each succeedingregion succeeding a region on one end of a region of interest columnarranged in a first direction in a two-dimensional image, a portion thatdoes not overlap the one-dimensional pixel array of a region adjacent tothe succeeding region on the side of the region on one end of the regionof interest column is obtained. Subsequently, a connectedone-dimensional pixel array is generated by connecting the obtainedportion and the one-dimensional pixel array corresponding to the regionon one end.

Returning to FIG. 6, next, in step S1024, an acquisition module 124loads, to the RAM 13, a weight coefficient matrix (weight coefficientgroup) stored in the secondary storage device 14. In this embodiment, aweight coefficient matrix having a size of nine rows (the number ofpixels in a setting region=3×3) and three columns (the number of outputchannels) and whose elements are weight coefficients will be loaded fromthe secondary storage device 14 to the RAM 13.

In step S1025, an arithmetic operation module 125 performs convolutionprocessing by using the connected one-dimensional pixel array and theweight coefficient matrix. Letting I be the connected one-dimensionalpixel array, F be weight coefficient matrix, and D be an output vector,the arithmetic operation module 125 will perform the arithmeticoperation processing according toD(i,n)=ΣF(j,n)I(i−j)  (1)where Σ represents that F(j,n)I(i−j) will be added to all j, irepresents an output vector index, n represents an output vector and aweight coefficient channel, and j is a weight coefficient index. FIG. 8is a view schematically showing the convolution processing by thearithmetic operation module 125.

As shown in FIG. 8, the row component and the column component of aweight coefficient matrix 301 correspond to a filter size (that is, 3×3)and the output channel n, respectively. It is preferable for eachelement (weight coefficient) of the weight coefficient matrix 301 to berearranged in this manner in advance. The convolution processing of aconnected one-dimensional pixel array 890 and the weight coefficientmatrix 301 is implemented by calculating the sum of matrix products eachobtained between the weight coefficient matrix and a reference range byshifting the reference range in the connected one-dimensional pixelarray 890 that has been generated for one face image. A reference range303 in the connected one-dimensional pixel array 890 corresponds to theone-dimensional pixel array of the setting region positioned in the topleft corner of the face image 201, and a reference range 304 correspondsto the one-dimensional pixel array of the setting range obtained byshifting, to the right by one pixel, the setting region at the top leftcorner of the face image 201. The above-described equation (1) is usedto calculate the sum of products each obtained between the weightcoefficient matrix and a reference range by shifting the reference rangein this manner. In other words, vector matrix multiplication isrepeatedly performed by shifting the reference range. The memory can beaccessed continuously at the time of the convolution by causing theconversion module 123 to rearrange the setting region elements inadvance so that the elements will be continuous (generate a connectedone-dimensional pixel array) as described above in this manner, and thusspeedup can be expected. Note that an activation function or the likemay be applied to each element after the convolution processing.

Returning to FIG. 4, next, a pooling processing module 112 executespooling processing on the result of the convolution processing by theconvolution processing module 111. This processing is the processing ofa so-called pooling (subsampling) layer, and a known method may beemployed. For example, a method described in the following literaturecan be used.

-   P. Sermanet, S. Chintala, and Y. LeCun. Convolutional neural    networks applied to house numbers digit classification. In    International Conference on Pattern Recognition (ICPR 2012), 2012.

A convolution processing module 113 has the same arrangement (FIG. 5) asthe convolution processing module 111 and performs the same processingas the convolution processing module 111 on the result obtained by thepooling processing module 112. Since the convolution processingperformed by the convolution processing module 113 is the same as thatperformed by the convolution processing module 111 other than in thefact that different input size and weight coefficient are used, adescription related to the convolution processing module 113 will beomitted. A pooling processing module 114 executes the same poolingprocessing as the pooling processing module 112 on the result of theconvolution processing by the convolution processing module 113.

A full connection processing module 115 performs so-called fullyconnected (Fullconnect) layer processing. More specifically, the fullconnection processing module 115 performs a vector matrix multiplicationoperation of multiplying an input vector (the result of the poolingprocessing by the pooling processing module 114) by the weightcoefficient matrix and outputs the arithmetic operation result (vector)as feature amount of the face image.

Returning to FIG. 3, next, in step S1015, a similarity calculationmodule 105 loads the registration dictionary stored in the secondarystorage device 14 to the RAM 13. A description of the registrationdictionary will be given here. A plurality of sets, each set includingthe feature amount obtained by the feature extraction module 103 from aface image and the identification information of a person (for example,a number or name corresponding to the person) corresponding to thefeature amount, are registered in the registration dictionary. In theregistration function (to be described later), the user will use theinput device 17 to input the identification information of the personcorresponding to the feature amount obtained by the feature extractionmodule 103 from a face image, and an acquisition module 108 will acquirethe input identification information. Subsequently, a dictionaryregistration module 104 will create a set of a feature amount, which isobtained by the feature extraction module 103 from the face image, andidentification information, which is related to the feature amount thathas been acquired by the acquisition module 108, and will register theset of the feature amount and the identification information in theregistration dictionary. Note that the registration dictionary mayinclude a set corresponding to each person of a plurality of people ormay include a plurality of sets for one person.

In step S1016, the similarity calculation module 105 obtains thesimilarity between the feature amount obtained by the feature extractionmodule 103 from the face image in step S1014 and each feature amountincluded in the registration dictionary loaded to the RAM 13 in stepS1015. There are various kinds of methods for obtaining the similaritybetween feature amounts, and any method can be employed. For example, acosine similarity S between feature amounts can be obtained by usingS=cos θ=x·y/|x∥y|  (2)where “x·y” represents an inner product operation of a feature vector(feature amount) x and a feature vector (feature amount) y, |x| and |y|represent the size of the feature vector x and the size of the featurevector y, respectively, and S represents the similarity between thefeature vector x and the feature vector y.

In step S1017, a determination module 106 determines, based on thesimilarity obtained by the similarity calculation module 105, whichperson is (or is not), among the people whose feature amounts areregistered in the registration dictionary, the person corresponding tothe feature amount obtained by the feature extraction module 103 in stepS1014. For example, the determination module 106 specifies the maximumsimilarity among the similarities obtained by the similarity calculationmodule 105 for the respective feature amounts registered in theregistration dictionary. If the specified maximum similarity is equal toor more than a threshold, the determination module 106 acquires theidentification information registered in the registration dictionarythat has been registered as a set with the feature amount from which themaximum similarity was obtained. For example, in a case in which thesimilarity between the feature amount obtained in step S1014 and afeature amount A registered in the registration dictionary correspondsto the above-described maximum similarity, the determination module 106will determine that the feature amount obtained in step S1014 is thefeature amount of a person corresponding to the feature amount A. Thus,in this case, the determination module 106 will read out theidentification information that forms the set with the setting region Aas the identification information of the person corresponding to thefeature amount obtained in step S1014.

On the other hand, if the specified maximum similarity is less than thethreshold, the determination module 106 will determine that the featureamount obtained by the feature extraction module 103 in step S1014 doesnot match the feature amount of any person whose feature point isregistered in the registration dictionary.

Note that in a case in which similarities are obtained for the pluralityof feature amounts for the same person in the registration dictionary,the maximum similarity among the obtained similarities will bedetermined to be the similarity corresponding to the person. Theauthentication result of the face image is obtained in this manner instep S1017.

In step S1018, a display module 107 causes the external output device 16to display the authentication result acquired by the determinationmodule 106. For example, if the determination module 106 has acquiredthe identification information from the registration dictionary, thedisplay module 107 may read out the person information stored in thesecondary storage device 14 in association with the identificationinformation and cause the external output device 16 to display theperson information. The person information stored in the secondarystorage device 14 in association with the identification information is,for example, text information (name, age, sex, and the like) concerningthe person or the image of the person corresponding to theidentification information. Note that it may be arranged so that themaximum similarity described above will be displayed together with theperson information. In addition, if the determination module 106determines that “the feature amount obtained by the feature extractionmodule 103 in step S1014 does not match the feature amount of any personwhose feature point is registered in the registration dictionary”, thedisplay module 107 will cause the external output device 16 to displayimages and characters indicating this state.

Note that in a case in which the authentication result notification isto be performed by audio, the display module 107 will output a soundcorresponding to the authentication result from the external outputdevice 16, and in a case in which the authentication result notificationis to be performed by lighting an LED lamp or by causing the LED lamp tolight a pattern, the light or the lighting pattern of the LED lamp willbe controlled in accordance with the authentication result.

When the processes of steps S1014 to S1018 have been performed for allof the face images detected from the captured image, the processadvances to step S1019. In step S1019, the CPU 11 determines whether theuser has input a processing end instruction by operating the inputdevice 17. As a result of this determination, if it is determined thatthe user has input a processing end instruction by operating the inputdevice 17, the processing according to the flowchart of FIG. 3 iscompleted. On the other hand, if it is determined that the user has notinput a processing end instruction by operating the input device 17, theprocess returns to step S1011.

Note that although the authentication processing has been performed forall of the captured images obtained in step S1011 in the abovedescription, it may be set so that the authentication processing will beperformed for some of captured images. For example, the authenticationprocessing may be performed on a captured image every couple of framesor the authentication processing may be performed by clustering thefeature amounts extracted from a captured image and using arepresentative feature amount selected from one cluster.

In addition, although deep neural network processing shown in FIG. 4 wasused for feature amount extraction, the deep neural network need nothave this arrangement. For example, the number of convolution processingoperations may be increased or other processing may be added.

The registration function of the information processing apparatus 1 willbe described next. FIG. 9 shows a flowchart corresponding to theregistration function. Note that the information processing apparatus 1has been activated before the start of the processing according to theflowchart of FIG. 9 and is in a state in which the processing to bedescribed below can be started. In addition, assume that the camera 2has been activated and is in a moving image capturing state as amonitoring camera. In this embodiment, assume that the same camera(camera 2) will be used as the camera to be used at the time of theexecution of the registration function and at the time of the executionof the authentication function. Such an arrangement will allow a featureamount, which is based on a captured image obtained under the same imagecapturing condition (illumination condition, direction of the face, andthe like) as the authentication location, to be registered in theregistration dictionary, and thus improvement of the authenticationaccuracy can be expected. Note that it may be arranged so that a camerafor dictionary registration will be installed in another location andused at the execution of the registration function. However, since theimage capturing conditions will differ from those of themonitoring/authentication location in this case, attention is needed todetermine whether the authentication accuracy will be sufficient.

In step S1031, in the same manner as the above-described step S1011, theimage acquisition module 101 acquires a captured image received from thecamera 2 by the communication device 15 and stored in the RAM 13 andconverts the captured image into a single-channel grayscale image. Notethat it is preferable to prepare a registration mode for the dictionaryregistration and acquire an image in accordance with user input.

Next, in step S1032, in the same manner as the above-described stepS1012, the face detection module 102 detects the region of an object(the face of a person in this embodiment) from the captured imageacquired by the image acquisition module 101 and extracts an image inthe detected region as a face image. Note that if a face cannot bedetected from the captured image, it is preferable to perform processingto prompt the user to acquire a captured image again.

Next, in step S1033, in the same manner as the above-described stepS1014, the feature extraction module 103 extracts a feature amount fromthe face image by using the CNN that has been generated in advance.Since the user will operate the input device 17 to input theidentification information of the person corresponding to the face imagehere, the acquisition module 108 will acquire the input identificationinformation in step S1034.

Subsequently, in step S1035, the dictionary registration module 104 willregister the feature amount extracted in step S1033 and theidentification information acquired in step S1034 as a set in theregistration dictionary. The registration dictionary is stored in thesecondary storage device 14 or the RAM 13.

Second Embodiment

The first embodiment exemplified a case in which a captured image isobtained as a single-channel grayscale image and convolution processingis performed on a face image (single-channel grayscale image) extractedfrom such a captured image. However, there are many cases in which acaptured image which is a multi-channel image such as a color image willbe input in practice. This embodiment will describe convolutionprocessing performed on a face image (multi-channel image) that has beendetected from a captured image which is a multi-channel image.Differences from the first embodiment will be described hereinafter, andcomponents and arrangements are the same as those of the firstembodiment unless particularly mentioned. In this embodiment, thefollowing processing will be executed in accordance with the flowchartof FIG. 6.

In step S1021, an acquisition module 121 acquires a face image as inputdata. Since the captured image is a multi-channel image in thisembodiment, the face image detected from the captured image is also amulti-channel image.

In step S1022, a setting module 122 will set, for each channel image ofthe face image acquired in step S1021, setting regions two-dimensionallyon the channel image so adjacent setting regions will overlap each otherin the same manner as in the first embodiment.

In step S1023, a conversion module 123 will convert a two-dimensionalpixel array in each setting region set by the setting module 122 into aone-dimensional pixel array, and generate a single connectedone-dimensional pixel array based on the one-dimensional pixel array ofeach setting region. The operation of the conversion module 123according to this embodiment will be described with reference to theexamples shown in FIGS. 10A and 10B. In FIGS. 10A and 10B, a connectedone-dimensional pixel array has been generated from the setting regionsof a face image that has two channel images. Note that, as describedabove, the processing order is not limited to that shown in FIGS. 10Aand 10B.

As shown in FIG. 10A, the conversion module 123 converts thetwo-dimensional pixel array in a setting region 403 at the top leftcorner of a first channel image (a channel image of ch=0) 401 of theface image into a one-dimensional pixel array 405. Reference symbols A1to A9 denote the pixel values of the respective pixels forming thetwo-dimensional pixel array in the setting region 403. The conversionmodule 123 also converts the two-dimensional pixel array, in a settingregion 404 at the top left corner of a second channel image (a channelimage of ch=1) 402 of the face image, into a one-dimensional pixel array406. Reference symbols B1 to B9 denote the pixel values of therespective pixels forming the two-dimensional pixel array in the settingregion 404. The conversion module 123 then generates a connectedone-dimensional pixel array 407 integrating the one-dimensional pixelarray 405 and the one-dimensional pixel array 406. As shown in FIG. 10A,the connected one-dimensional pixel array 407 is an array in which theelements forming the one-dimensional pixel array 405 and the elementsforming the one-dimensional pixel array 406 are arranged alternately,and has, as a result, an arrangement of A1, B1, A2, B2, . . . , A9, B9.This arrangement is an arrangement in which the channels will becontinuous.

Next, as shown in FIG. 10B, the conversion module 123 converts thetwo-dimensional pixel array in a setting region 408 obtained by shiftingthe setting region 403 to the right by one pixel in the first channelimage 401 into a one-dimensional pixel array 410. Reference symbols A4to A12 denote the pixel values of the respective pixels forming thetwo-dimensional pixel array in the setting region 408. The conversionmodule 123 also converts, as shown in FIG. 10B, the two-dimensionalpixel array in a setting region 409 obtained by shifting the settingregion 404 to the right by one pixel in the second channel image 402into a one-dimensional pixel array 411. Reference symbols B4 to B12denote the pixel values of the respective pixels forming thetwo-dimensional pixel array in the setting region 409. The conversionmodule 123 then generates a portion 490 in which the elements (A10 toA12) that do not overlap the one-dimensional pixel array 405 in theone-dimensional pixel array 410 and the elements (B10 to B12) that donot overlap the one-dimensional pixel array 406 in the one-dimensionalpixel array 411 are alternately arranged. The portion 490 will have anarrangement of A10, B10, A11, B11, A12, and B12. Next, the conversionmodule 123 will connect the generated portion 490 to the right of theconnected one-dimensional pixel array 407. That is, this connectedone-dimensional pixel array shares the elements belonging to a regionwhere the setting regions overlap. Subsequently, the same processingwill be performed until the rightmost setting region, and uponcompletion of the processing of the rightmost setting region,two-dimensional pixel arrays in setting regions each obtained byshifting one of the setting regions 403 and 404 by one pixel below willbe converted into one-dimensional pixel arrays in the same manner as inthe first embodiment. The elements of the one-dimensional pixel arrayswill be alternately arranged and connected into a connectedone-dimensional pixel array. The same processes as described above willbe performed hereinafter.

In this manner, by generating a connected one-dimensional pixel array soas to create a continuous channel, the arrangement of the elements ofthe one-dimensional pixel array corresponding to each setting regionwill become continuous. To describe this in more general terms, aconnected one-dimensional pixel array is generated so that a dimensionother than the dimension of the arrangement positions (the horizontaldirection and the vertical direction of the face image in thisembodiment) of each setting region will be continuous. This will allowsubsequent processes to be processed by a convolution in the same manneras the first embodiment. For example, if the one-dimensional pixel array405 and the one-dimensional pixel array 406 are connected as they are,the elements belonging to the next setting region excluding theoverlapping portion will be discontinuous, and the convolutionprocessing will not be able to be performed.

Returning to FIG. 6, the following processes of steps S1024 and S1025are the same as those of the first embodiment. Note that although thecaptured image (face image) has been described as including two channelimages in this embodiment, the number of channels of the captured image(face image) is not limited to two. However, the generation method ofthe connected one-dimensional pixel array is the same as that describedabove even in this case. For example, assume that the number of channelsof the captured image (face image) is CH (an integer equal to or morethan 3). That is, it is assumed that a captured image will include achannel image of channel number ch=1, a channel image of channel numberch=2, . . . , and a channel image of channel number ch=CH. In this case,the connection target is an array that has been obtained by connectingan element array in which an Nth element from the leftmost end of anone-dimensional pixel array of each channel image has been arranged in achannel image order, an element array in which each (N+1)th element fromthe leftmost end has been arranged in the channel image order, . . . ,and an element array in which each rightmost end element has beenarranged in the channel image order. A channel image order points to anascending order of ch. Here, the “Nth element” (an identical positionelement) is N=1 in the case of a leftmost setting region, and N (N=10 inthe case of FIG. 10B) corresponding to the first element of theconnection target in the case of a setting region other than theleftmost setting region.

Third Embodiment

The first and second embodiments have described convolution processingperformed on a two-dimensional image. However, the embodiments describedabove can be applied to convolution processing performed on a largerdimensional input. For example, the following literature disclosesconvolution processing performed on three-dimensional input data, andthe above-described embodiments can be applied to this example.

-   D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri.    Learning spatiotemporal features with 3D convolutional networks. In    ICCV, 2015.

Differences from the first embodiment will be described hereinafter, andcomponents and arrangements are the same as those of the firstembodiment unless particularly mentioned. This embodiment will describea case in which a feature amount of a time-series sequence (athree-dimensional object obtained by stacking the face images of aplurality of frames) of faces images (grayscale images) detected fromthe respective captured images of a plurality of frames that havecaptured a single face is obtained. In this embodiment, the followingprocessing will be executed in accordance with the flowchart of FIG. 6.

In step S1021, an acquisition module 121 acquires a time series sequence(a three-dimensional object obtained by stacking the face images of aplurality of frames) of face images. In step S1022, the setting module122 sets setting regions three-dimensionally in the three-dimensionalobject so that adjacent setting regions (three-dimensional regions inthis embodiment) will partially overlap each other. In step S1023, aconversion module 123 converts the three-dimensional pixel array in eachsetting region set by a setting module 122 into a two-dimensional pixelarray and generates a single connected two-dimensional pixel array basedon the two-dimensional pixel arrays of the respective setting regions.The operation of the conversion module 123 according to this embodimentwill be described with reference to the examples shown in FIG. 11A to11D. Note that the processing order is not limited to that shown inFIGS. 11A to 11D.

In FIG. 11A, a three-dimensional object 501 is a three-dimensionalobject obtained by stacking the face images. An x-direction correspondsto the horizontal direction, a y-direction corresponds to a verticaldirection in of the face images, and a z-direction corresponds to thestacking direction (that is, the time (frame) direction) of the faceimages.

As shown in FIG. 11A, the conversion module 123 converts thethree-dimensional pixel array in a setting region 502 whose position atthe top left corner at the front side is at a position 1199 at the topleft corner on the front side of the three-dimensional object 501 into atwo-dimensional pixel array 503. In FIG. 11A, reference symbols fx, fy,and fz denote the size in the x direction, the size in the y direction,and the size in the z direction, respectively, of the setting region502. The two-dimensional pixel array 503 is an array obtained byarranging partial images included in the setting region 502 from top tobottom in the order of the frames, and a reference symbol fx denotes thesize in the horizontal direction, and reference symbol fy×fz denotes thesize in the vertical direction. In this manner, three-dimensional data(three-dimensional pixel array) is converted into two-dimensional data(two-dimensional pixel array) by combining the y-axis and the x-axis.

Next, as shown in FIG. 11B, the conversion module 123 converts thethree-dimensional pixel array in a setting region 504 obtained byshifting the setting region 502 in the x-axis direction (a directionperpendicular to the z direction) by one pixel into a two-dimensionalpixel array. As described above, since there is an overlapping regionbetween the setting region 502 and the two-dimensional pixel array 503which are adjacent to each other, an overlapping portion will begenerated as a result between the two-dimensional pixel arraycorresponding to the setting region 504 and the two-dimensional pixelarray 503 corresponding to the setting region 502. Hence, the conversionmodule 123 will acquire, from the two-dimensional pixel arraycorresponding to the setting region 504, a portion 590 (a regionindicated by slanted lines in FIG. 11B) which does not overlap thetwo-dimensional pixel array 503 in the two-dimensional pixel arraycorresponding to the setting region 504. Subsequently, the conversionmodule 123 will generate a connected two-dimensional pixel array 599obtained by connecting the acquired portion 590 to the right of thetwo-dimensional pixel array 503. That is, this connected two-dimensionalpixel array shares the elements belonging to a region where the settingregions overlap. Subsequently, the conversion module 123 updates theconnected two-dimensional pixel array by performing the same processingon a setting region obtained by shifting the setting region 504 in the xdirection.

Next, assume that a setting region obtained by shifting the settingregion 502 in the x direction has reached the rightmost end of thethree-dimensional object 501, and the connection target of the rightmostsetting region has been connected to the connected two-dimensional pixelarray. Then, as shown in FIG. 11C, the three-dimensional pixel array ina setting region 505 obtained by shifting the setting region 502 in they direction by one pixel is converted into a two-dimensional pixel array580. As shown in FIG. 11C, the conversion module 123 connects thetwo-dimensional pixel array 580 to the right of the connectedtwo-dimensional pixel array 599 at this point. Subsequently, the sameprocessing is performed until the connection target of the settingregion at the bottom right corner on the front side of thethree-dimensional object 501 is connected to the connectedtwo-dimensional pixel array.

When every setting region on the front side of the three-dimensionalobject 501 has been connected to the connected two-dimensional pixelarray, a two-dimensional pixel array 585 of a setting region 507obtained by shifting the setting region 502 in the z direction by onepixel is obtained as shown in FIG. 11D. The conversion module 123 willthen connect the obtained two-dimensional pixel array 585 to the footand the leftmost end of the connected two-dimensional pixel array.Subsequently, as described above, the connected two-dimensional pixelarray is generated by sequentially connecting the connection target ofeach setting region which has the same z-direction position (z position)but different x-direction position (x position) and y-direction position(y position) to the right of the two-dimensional pixel array 585.

In this manner, the connected two-dimensional pixel array generated inthis embodiment is an array in which “connected two-dimensional pixelarrays, each obtained by connecting the two-dimensional pixel arrays orthe connection targets of setting regions in the same z position” arearranged from top to bottom (or from bottom to top) in the z-positionorder.

Returning to FIG. 6, next, in step S1024, an acquisition module 124loads a three-dimensional weight coefficient matrix (weight coefficientgroup) stored in a secondary storage device 14 into a RAM 13. Next, instep S1025, an arithmetic operation module 125 executes convolutionprocessing by using the connected two-dimensional pixel array and thethree-dimensional weight coefficient matrix. FIG. 12 is a viewschematically showing the convolution processing performed by thearithmetic operation module 125.

Reference symbols fx, fy, and fz denote a size in the x direction, asize in the y direction, and a size in the z direction, respectively, ofa three-dimensional weight coefficient matrix 601. A two-dimensionalweight coefficient matrix 602 is a weight coefficient matrix obtained byconnecting, in the vertical direction, two-dimensional weight matricescorresponding to respective z positions in the three-dimensional weightcoefficient matrix 601, and a reference symbol fx denotes the size inthe horizontal direction and reference symbol fy×fz denotes the size inthe vertical direction. The two-dimensional weight coefficient matrix602 converts three-dimensional data into two-dimensional data bycombining the x-axis and the y-axis in the same manner as describedabove. A three-dimensional convolution is implemented by performingconvolution processing of the two-dimensional weight coefficient matrix602 and a connected two-dimensional pixel array 1201. That is, acalculation is performed so that the convolution of three-dimensionaldata will result in the convolution of two-dimensional data. To describethis in more general terms, a calculation is performed so that a higherdimensional data convolution will result in a lower dimensional dataconvolution. This convolution of two-dimensional data can be performedby using the method described in the first embodiment.

Subsequently, the above-described feature amount of thethree-dimensional object is obtained by performing the above-describedpooling processing and the same kind of three-dimensional convolutionprocessing as the above-described three-dimensional convolutionprocessing on this convolution processing. The subsequent processing isthe same as that in the first embodiment.

Fourth Embodiment

The first to third embodiments used several schematic views andprocedures to describe the shapes of transformation vectors (theone-dimensional pixel array and the two-dimensional pixel array).However, the processing need not always be performed in these ways.Since it will ultimately result in the convolution of a lower dimensionvector and the weight coefficients, it is sufficient for the convertedvector to have a structure in which elements belonging to an overlappingportion of setting regions are shared as described above.

The first to third embodiments described a case in which thedimensionality of the input data is two or three. However, whatever thedimensionality of the input data is, any set of input data will resultin the following structure. That is, an element array in each region setso as to partially overlap in adjacent regions in a plane or a spacedefined by the input data is converted into a lower dimension elementarray as an element array of a lower dimension. Subsequently, aconnected element is generated by connecting all or some of the lowerdimension element arrays so that the overlapping portions will be sharedin the converted lower dimension element arrays, and the feature amountof the input data is obtained based on the convolution of the connectedelements and the weight coefficients. Note that it may be set so that atleast one set of adjacent regions will partially overlap each other.

In addition, if there are a plurality of input data items, theprocessing of the embodiments described above can be applied to each ofthe plurality of input data items or a calculation can be performed byconnecting the transformation vectors generated for the plurality ofinput data items and performing the convolution once. In such a case,since the portion of the convolution over the plurality of input dataitems is wasteful as processing when convolving the weight coefficientsto the transformation vector, the calculation needs to be skipped oronly effective elements needs to be extracted by executing rearrangementor the like.

Furthermore, although an example in which deep neural network processingthat includes convolution processing is performed for facialauthentication was described above, the purpose of the feature amountobtainment processing described above is not limited to facialauthentication. For example, the feature amount calculation processingdescribed in the first to third embodiments may be applied to an imagerecognition operation other than facial authentication or to convolutionwhich is not deep neural network processing, for example, simple imagefilter processing or the like.

Fifth Embodiment

The camera 2 and the information processing apparatus 1 were describedas separate apparatuses in the embodiments described above. However, thecamera 2 and the information processing apparatus 1 may be integratedinto a single apparatus. That is, the camera 2 may be formed so as toexecute the functions described above as the functions of theinformation processing apparatus 1.

In addition, in the above-described embodiments, a result, such as anauthentication result, of processing performed by using feature amountswere notified to a user by display, audio output, lighting an LED lamp,and causing the LED lamp to light a pattern, but the notificationmethods are not limited to these. For example, the notification may beperformed by transmitting an email to a specific notificationdestination.

In the above-described embodiments, the functions of the informationprocessing apparatus 1 were implemented by a CPU 11 executing computerprograms. However, the same functions may be implemented by usinghardware. For example, some or all of the functional modules shown inFIG. 2 may be implemented by hardware. A dedicated circuit (ASIC), aprocessor (a reconfigurable processor, a DSP, or the like), and the likemay be used as the hardware. In addition, the same functions may beimplemented by using a GPU. Furthermore, it may be arranged so that eachcomputer program described above will be read out from a storage mediumsuch as a CD-ROM, a DVD-ROM, or the like or acquired by receiving thecomputer program from the outside via a network, and the acquiredcomputer program will be executed by the CPU 11.

Also, one camera transmitted a captured image to the informationprocessing apparatus 1 in the above-described embodiments. However, thepresent invention is not limited to this, and a plurality of cameras maybe used. In addition, although the above-described embodimentsexemplified a case in which the information processing apparatus 1acquired the captured image from the camera 2, the acquisition method ofthe captured image is not limited to a specific acquisition method. Forexample, it may be arranged so that the information processing apparatus1 will acquire, from a server apparatus, captured images which have beenobtained in advance and stored in the server apparatus.

In addition, although a feature amount was extracted from an entire faceimage in the above-described embodiments, it may be set so that thefeature amount will be extracted from a partial region of a face image.For example, the feature amount may be extracted from a partial regionset by using a specific facial organ (such as eyes, a nose, or the like)as a reference. A known technique (for example, a method disclosed inJapanese Patent Laid-Open No. 2009-211177) may be used as the method ofdetecting the position of the organ in the face image. Furthermore,dimensional compression and quantization of the extracted feature amountmay be performed.

In addition, the above-described embodiments described an example inwhich authentication was executed for every face image detected from acaptured image. However, it may be set so that the authenticationprocessing will be performed on only a specific face image among theface images of the captured image. That is, it may be set so that theprocesses of steps S1014 to S1018 described above will be performed onlyon a specific face image. A specific face image is a face image thatsatisfies specific conditions, for example, a face image of a size equalto or more than a predetermined size, a face image whose occupationratio in the captured image is equal to or more than a predeterminedvalue, a face image selected by the user on the captured image, or thelike.

In addition, although the information processing apparatus 1 wasdescribed as having both the authentication function and theregistration function in the above-described embodiments, it may beseparated into an apparatus for executing the authentication functionand an apparatus for executing the registration function.

Other Embodiments

Embodiment(s) of the present invention can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2018-077796, filed Apr. 13, 2018, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An information processing apparatus comprising: aconversion unit configured to convert an element array, in each regionset so as to partially overlap at least one set of adjacent regions ininput data, into a lower dimension element array of which dimension islower than that of the element array; a generation unit configured togenerate a connected element by connecting some or all of the lowerdimension element arrays converted by the conversion unit so that anoverlapping portion in each of the lower dimension element arrays willbe shared; and a calculation unit configured to obtain a feature amountof the input data based on convolution of the connected elements and aweight coefficient.
 2. The apparatus according to claim 1, wherein theinput data is a two-dimensional image and the lower dimension elementarray is a one-dimensional pixel array.
 3. The apparatus according toclaim 2, wherein the conversion unit converts, into a one-dimensionalpixel array, a two-dimensional pixel array in each regiontwo-dimensionally set on the two-dimensional image so that adjacentregions will partially overlap each other.
 4. The apparatus according toclaim 3, wherein the generation unit acquires, from a one-dimensionalpixel array of each succeeding region succeeding a region on one end ofa region of interest column arranged in a first direction in thetwo-dimensional image, a portion which does not overlap aone-dimensional pixel array of a region adjacent to the succeedingregion on the side of the region on one end of the region of interestcolumn, and the generation unit generates a connected one-dimensionalpixel array by connecting the portion and a one-dimensional pixel arraycorresponding to the region on one end.
 5. The apparatus according toclaim 4, wherein the calculation unit obtains the feature amount of thetwo-dimensional image based on the convolution of the connectedone-dimensional pixel array and the weight coefficient.
 6. The apparatusaccording to claim 2, wherein the two-dimensional image is each channelimage in a multi-channel image.
 7. The apparatus according to claim 6,wherein the generation unit generates a connected one-dimensional pixelarray by connecting an element array obtained by arranging, in a channelimage order, elements at identical positions in the one-dimensionalpixel arrays corresponding to the regions on one end in the respectivechannel images and an element array obtained by arranging, in thechannel image order, elements at identical positions in the portionscorresponding to the respective channel images.
 8. The apparatusaccording to claim 2, wherein the calculation unit performs firstpooling processing on the result of the convolution of the connectedone-dimensional pixel array and the weight coefficient.
 9. The apparatusaccording to claim 8, wherein the calculation unit performs secondconvolution and second pooling processing on a result of the firstpooling processing.
 10. The apparatus according to claim 9, wherein thecalculation unit obtains the feature amount by performing fullconnection processing on a result of the second pooling processing. 11.The apparatus according to claim 2, wherein the two-dimensional image isan image of a human face.
 12. The apparatus according to claim 1,wherein the input data is a three-dimensional object obtained bystacking a plurality of two-dimensional images.
 13. The apparatusaccording to claim 12, wherein the conversion unit converts athree-dimensional pixel array in each region set in thethree-dimensional object so that adjacent regions will partially overlapeach other into a two-dimensional pixel array.
 14. The apparatusaccording to claim 13, wherein the generation unit acquires, from atwo-dimensional pixel array of a succeeding region succeeding a regionon one end of a region of interest column arranged in a first directionperpendicular to a stacking direction of the three-dimensional object, aportion which does not overlap a two-dimensional pixel array of a regionadjacent to the succeeding region on the side of the region on one endof the region of interest column, and the generation unit generates aconnected two-dimensional pixel array by connecting the portion and atwo-dimensional pixel array corresponding to the region on the region onone end.
 15. The apparatus according to claim 14, wherein thecalculation unit obtains the feature amount of the three-dimensionalobject based on the convolution of the connected two-dimensional pixelarray and the weight coefficient.
 16. The apparatus according to claim1, further comprising: a registration unit configured to register thefeature amount and information related to an object input by a user in aregistration dictionary.
 17. The apparatus according to claim 16,further comprising: an authentication unit configured to authenticatethe input data based on the similarity between the feature amountobtained by the calculation unit and the feature amount registered inthe registration dictionary.
 18. The apparatus according to claim 17,further comprising: an output unit configured to output a result of theauthentication by the authentication unit.
 19. An information processingmethod performed by an information processing apparatus, the methodcomprising: converting an element array, in each region set so as topartially overlap at least one set of adjacent regions in input data,into a lower dimension element array of which dimension is lower thanthat of the element array; generating a connected element by connectingsome or all of the lower dimension element arrays converted in theconverting so that an overlapping portion in each of the lower dimensionelement arrays will be shared; and obtaining a feature amount of theinput data based on convolution of the connected elements and a weightcoefficient.
 20. A non-transitory computer-readable storage mediumstoring a computer program for causing a computer to function as aconversion unit configured to convert an element array, in each regionset so as to partially overlap at least one set of adjacent regions ininput data, into a lower dimension element array of which dimension islower than that of the element array of a lower dimension; a generationunit configured to generate a connected element by connecting some orall of the lower dimension element arrays converted by the conversionunit so that an overlapping portion in each of the lower dimensionelement arrays will be shared; and a calculation unit configured toobtain a feature amount of the input data based on convolution of theconnected elements and a weight coefficient.